ArticleIEEE transactions on biomedical circuits and systems2023
FPGA-Based In-Vivo Calcium Image Decoding for Closed-Loop Feedback Applications.
Article in IEEE transactions on biomedical circuits and systems, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- A feedback-driven brain organoid platform enables automated maintenance and high-resolution neural activity monitoring.Internet of things (Amsterdam, Netherlands) · 2025Article
- From microelectrode arrays to all-optical and multimodal neural interfaces: emerging platforms for spatiotemporal interrogation ofFrontiers in synaptic neuroscience · 2025Review
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8 authors.
Funding
Abstract
Miniaturized calcium imaging is an emerging neural recording technique that has been widely used for monitoring neural activity on a large scale at a specific brain region of rats or mice. Most existing calcium-image analysis pipelines operate offline. This results in long processing latency, making it difficult to realize closed-loop feedback stimulation for brain research. In recent work, we have proposed an FPGA-based real-time calcium image processing pipeline for closed-loop feedback applications. It can perform real-time calcium image motion correction, enhancement, fast trace extraction, and real-time decoding from extracted traces. Here, we extend this work by proposing a variety of neural network based methods for real-time decoding and evaluate the tradeoff among these decoding methods and accelerator designs. We introduce the implementation of the neural network based decoders on the FPGA, and show their speedup against the implementation on the ARM processor. Our FPGA implementation enables the real-time calcium image decoding with sub-ms processing latency for closed-loop feedback applications.
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Registered trials
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